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Conversion of Tactile Sign Language into English for Deaf/Dumb Interaction

Conversion of Tactile Sign Language into English for Deaf/Dumb Interaction

Urmila Shrawankar, Sayli Dixit
Copyright: © 2017 |Volume: 6 |Issue: 1 |Pages: 15
ISSN: 1947-928X|EISSN: 1947-9298|EISBN13: 9781522513490|DOI: 10.4018/IJNCR.2017010104
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MLA

Shrawankar, Urmila, and Sayli Dixit. "Conversion of Tactile Sign Language into English for Deaf/Dumb Interaction." IJNCR vol.6, no.1 2017: pp.53-67. http://doi.org/10.4018/IJNCR.2017010104

APA

Shrawankar, U. & Dixit, S. (2017). Conversion of Tactile Sign Language into English for Deaf/Dumb Interaction. International Journal of Natural Computing Research (IJNCR), 6(1), 53-67. http://doi.org/10.4018/IJNCR.2017010104

Chicago

Shrawankar, Urmila, and Sayli Dixit. "Conversion of Tactile Sign Language into English for Deaf/Dumb Interaction," International Journal of Natural Computing Research (IJNCR) 6, no.1: 53-67. http://doi.org/10.4018/IJNCR.2017010104

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Abstract

Natural language is the way of communication for normal human beings which includes spoken language, written and body gestures i.e. head gesture, hand gestures, facial expressions and lip motion etc. On the other hand, speech and hearing-impaired people uses sign language for communication which is not understandable for normal people thus they face problems of communication in society. In this problem interpreters are required but the human interpreters are costly and are not an efficient solution. Thus, there is a need of system which will translate the sign language into normal language which will be understandable by normal. The system proposed and explained in the paper is an efficient solution to this problem. In the system, sign recognition is done using CAMSHIFT and P2DHHM algorithm followed by Haar Cascade Classifier. After sign recognition, the language technology techniques of POS tagging and LALR parser are used to convert recognized sign words into English sentence. Till date no any system has worked on sentence framing. Results shows that this system produces 92% of accurate result which will bridge the gap between impaired and Normal people.

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